We choose the categories from the LVIS dataset [1]} for categorizing a long-tail subset of objects in Objaverse. We construct a 47K LVIS categorized object subset, called Objaverse-LVIS, comprised of objects uniquely assigned to one of 1156 LVIS categories. We perform these assignments by first selecting 500 candidate objects per category using a combination of predictions from a CLIP classification model and candidates suggested by terms in their metadata. This combined pool contains objects visually resembling the target category (from the CLIP features of their thumbnail images) that might have missing metadata, as well as visually unusual instances of a category that are accurately named or tagged. These 250k candidate objects were then manually filtered and their assigned categories verified by crowdworkers. Since we only presented 500 object candidates per class, many popular categories, such as chair or car, have substantially more objects that could be included in Objaverse-LVIS with future annotations.
